1. Field of the Invention
The present application relates generally to digital image processing and more particularly to the automated segmentation of image data.
2. Description of the Background Art
Image segmentation generally concerns selection and/or separation of an object or other selected part of an image dataset. The dataset is in general a multi-dimensional dataset that assigns data values to positions in a multi-dimensional geometrical space. In particular, the data values may be pixel values, such as brightness values, grey values or color values, assigned to positions in a two-dimensional plane.
Image segmentation has been a challenging problem in the field of computer vision for many years. The goal of a segmentation method is to pick out the objects or other selected parts in an image—given the image data. The uses include the fields of object recognition and point-and-click software. There have been specific image segmentation techniques designed when the content of the image is known ahead of time. Examples include segmentation techniques for x-ray images of the human body and for defect detection on silicon wafers. These methods are not applicable to other images.
Some of the other techniques target images in general. Some of these other techniques include region-based segmentation and the watershed algorithm. These suffer from the severe problem of separate objects being attached due to small overlaps where the boundary between objects is not clear. Prior systems have used a segmentation technique for generic images. The technique uses edge detection as the first step, and then uses a brush-filling technique as the second step. This segmentation technique performs quite well for a wide range of images. Due to the heavy computational cost, however, the algorithm is only implementable for real-time performance in dedicated hardware.